Overview

Dataset statistics

Number of variables25
Number of observations5453
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory201.0 B

Variable types

Text1
Numeric19
Categorical4
Boolean1

Alerts

age is highly overall correlated with segmento_edadHigh correlation
annual_income is highly overall correlated with monthly_inhand_salary and 3 other fieldsHigh correlation
monthly_inhand_salary is highly overall correlated with annual_income and 3 other fieldsHigh correlation
num_bank_accounts is highly overall correlated with interest_rate and 4 other fieldsHigh correlation
interest_rate is highly overall correlated with num_bank_accounts and 7 other fieldsHigh correlation
num_of_loan is highly overall correlated with interest_rate and 7 other fieldsHigh correlation
delay_from_due_date is highly overall correlated with num_bank_accounts and 4 other fieldsHigh correlation
num_of_delayed_payment is highly overall correlated with num_bank_accounts and 3 other fieldsHigh correlation
changed_credit_limit is highly overall correlated with payment_of_min_amountHigh correlation
num_credit_inquiries is highly overall correlated with interest_rate and 5 other fieldsHigh correlation
outstanding_debt is highly overall correlated with num_bank_accounts and 7 other fieldsHigh correlation
credit_history_age is highly overall correlated with interest_rate and 6 other fieldsHigh correlation
total_emi_per_month is highly overall correlated with num_of_loan and 1 other fieldsHigh correlation
amount_invested_monthly is highly overall correlated with annual_income and 1 other fieldsHigh correlation
monthly_balance is highly overall correlated with annual_income and 2 other fieldsHigh correlation
prop_deuda_sueldo_anual is highly overall correlated with annual_income and 7 other fieldsHigh correlation
prop_emi_sueldo is highly overall correlated with num_of_loan and 2 other fieldsHigh correlation
payment_of_min_amount is highly overall correlated with num_bank_accounts and 7 other fieldsHigh correlation
segmento_edad is highly overall correlated with ageHigh correlation
customer_id has unique valuesUnique
credit_utilization_ratio has unique valuesUnique
prop_deuda_sueldo_anual has unique valuesUnique
num_bank_accounts has 234 (4.3%) zerosZeros
num_of_loan has 631 (11.6%) zerosZeros
delay_from_due_date has 71 (1.3%) zerosZeros
num_of_delayed_payment has 81 (1.5%) zerosZeros
changed_credit_limit has 103 (1.9%) zerosZeros
num_credit_inquiries has 231 (4.2%) zerosZeros
total_emi_per_month has 588 (10.8%) zerosZeros
monthly_balance has 172 (3.2%) zerosZeros
prop_emi_sueldo has 588 (10.8%) zerosZeros

Reproduction

Analysis started2023-07-19 22:17:11.056107
Analysis finished2023-07-19 22:18:10.206513
Duration59.15 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

customer_id
Text

UNIQUE 

Distinct5453
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:10.607213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9391161
Min length9

Characters and Unicode

Total characters54198
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5453 ?
Unique (%)100.0%

Sample

1st rowCUS_0x21b1
2nd rowCUS_0x284a
3rd rowCUS_0x5407
4th rowCUS_0x4157
5th rowCUS_0x3e45
ValueCountFrequency (%)
cus_0x21b1 1
 
< 0.1%
cus_0x4100 1
 
< 0.1%
cus_0x5407 1
 
< 0.1%
cus_0x4157 1
 
< 0.1%
cus_0x3e45 1
 
< 0.1%
cus_0xff4 1
 
< 0.1%
cus_0x33d2 1
 
< 0.1%
cus_0x6070 1
 
< 0.1%
cus_0xfdb 1
 
< 0.1%
cus_0x3553 1
 
< 0.1%
Other values (5443) 5443
99.8%
2023-07-19T18:18:11.295528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6426
11.9%
C 5453
 
10.1%
S 5453
 
10.1%
_ 5453
 
10.1%
x 5453
 
10.1%
U 5453
 
10.1%
4 1550
 
2.9%
8 1498
 
2.8%
1 1488
 
2.7%
7 1481
 
2.7%
Other values (11) 14490
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19729
36.4%
Uppercase Letter 16359
30.2%
Lowercase Letter 12657
23.4%
Connector Punctuation 5453
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6426
32.6%
4 1550
 
7.9%
8 1498
 
7.6%
1 1488
 
7.5%
7 1481
 
7.5%
2 1472
 
7.5%
5 1469
 
7.4%
9 1460
 
7.4%
6 1452
 
7.4%
3 1433
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
x 5453
43.1%
a 1479
 
11.7%
b 1418
 
11.2%
c 1226
 
9.7%
e 1063
 
8.4%
d 1030
 
8.1%
f 988
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
C 5453
33.3%
S 5453
33.3%
U 5453
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 5453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29016
53.5%
Common 25182
46.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6426
25.5%
_ 5453
21.7%
4 1550
 
6.2%
8 1498
 
5.9%
1 1488
 
5.9%
7 1481
 
5.9%
2 1472
 
5.8%
5 1469
 
5.8%
9 1460
 
5.8%
6 1452
 
5.8%
Latin
ValueCountFrequency (%)
C 5453
18.8%
S 5453
18.8%
x 5453
18.8%
U 5453
18.8%
a 1479
 
5.1%
b 1418
 
4.9%
c 1226
 
4.2%
e 1063
 
3.7%
d 1030
 
3.5%
f 988
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6426
11.9%
C 5453
 
10.1%
S 5453
 
10.1%
_ 5453
 
10.1%
x 5453
 
10.1%
U 5453
 
10.1%
4 1550
 
2.9%
8 1498
 
2.8%
1 1488
 
2.7%
7 1481
 
2.7%
Other values (11) 14490
26.7%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.522281
Minimum14
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:11.508575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q125
median33
Q342
95-th percentile52
Maximum56
Range42
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.765548
Coefficient of variation (CV)0.32114604
Kurtosis-0.94367862
Mean33.522281
Median Absolute Deviation (MAD)9
Skewness0.15841301
Sum182797
Variance115.89702
MonotonicityNot monotonic
2023-07-19T18:18:11.711625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
26 186
 
3.4%
35 179
 
3.3%
43 173
 
3.2%
25 171
 
3.1%
28 166
 
3.0%
19 165
 
3.0%
32 165
 
3.0%
44 164
 
3.0%
20 164
 
3.0%
24 162
 
3.0%
Other values (33) 3758
68.9%
ValueCountFrequency (%)
14 39
 
0.7%
15 86
1.6%
16 96
1.8%
17 73
1.3%
18 109
2.0%
19 165
3.0%
20 164
3.0%
21 162
3.0%
22 141
2.6%
23 141
2.6%
ValueCountFrequency (%)
56 35
0.6%
55 84
1.5%
54 71
1.3%
53 67
1.2%
52 72
1.3%
51 64
1.2%
50 85
1.6%
49 63
1.2%
48 66
1.2%
47 83
1.5%

occupation
Categorical

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
Lawyer
369 
Journalist
367 
Mechanic
 
355
Accountant
 
354
_______
 
354
Other values (11)
3654 

Length

Max length13
Median length10
Mean length8.4384742
Min length6

Characters and Unicode

Total characters46015
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTeacher
2nd rowLawyer
3rd rowMedia_Manager
4th rowDoctor
5th rowEntrepreneur

Common Values

ValueCountFrequency (%)
Lawyer 369
 
6.8%
Journalist 367
 
6.7%
Mechanic 355
 
6.5%
Accountant 354
 
6.5%
_______ 354
 
6.5%
Architect 353
 
6.5%
Doctor 348
 
6.4%
Entrepreneur 345
 
6.3%
Scientist 343
 
6.3%
Engineer 336
 
6.2%
Other values (6) 1929
35.4%

Length

2023-07-19T18:18:11.924673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lawyer 369
 
6.8%
journalist 367
 
6.7%
mechanic 355
 
6.5%
accountant 354
 
6.5%
354
 
6.5%
architect 353
 
6.5%
doctor 348
 
6.4%
entrepreneur 345
 
6.3%
scientist 343
 
6.3%
engineer 336
 
6.2%
Other values (6) 1929
35.4%

Most occurring characters

ValueCountFrequency (%)
e 6047
13.1%
r 4755
10.3%
n 4087
 
8.9%
a 3700
 
8.0%
t 3486
 
7.6%
c 3454
 
7.5%
i 3367
 
7.3%
_ 2806
 
6.1%
o 1737
 
3.8%
M 1635
 
3.6%
Other values (18) 10941
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37782
82.1%
Uppercase Letter 5427
 
11.8%
Connector Punctuation 2806
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6047
16.0%
r 4755
12.6%
n 4087
10.8%
a 3700
9.8%
t 3486
9.2%
c 3454
9.1%
i 3367
8.9%
o 1737
 
4.6%
u 1374
 
3.6%
h 1039
 
2.7%
Other values (8) 4736
12.5%
Uppercase Letter
ValueCountFrequency (%)
M 1635
30.1%
A 707
13.0%
E 681
12.5%
D 668
12.3%
L 369
 
6.8%
J 367
 
6.8%
S 343
 
6.3%
T 331
 
6.1%
W 326
 
6.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2806
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43209
93.9%
Common 2806
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6047
14.0%
r 4755
11.0%
n 4087
9.5%
a 3700
 
8.6%
t 3486
 
8.1%
c 3454
 
8.0%
i 3367
 
7.8%
o 1737
 
4.0%
M 1635
 
3.8%
u 1374
 
3.2%
Other values (17) 9567
22.1%
Common
ValueCountFrequency (%)
_ 2806
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6047
13.1%
r 4755
10.3%
n 4087
 
8.9%
a 3700
 
8.0%
t 3486
 
7.6%
c 3454
 
7.5%
i 3367
 
7.3%
_ 2806
 
6.1%
o 1737
 
3.8%
M 1635
 
3.6%
Other values (18) 10941
23.8%

annual_income
Real number (ℝ)

HIGH CORRELATION 

Distinct5450
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178219.49
Minimum7019.435
Maximum23834698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:12.136982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7019.435
5-th percentile9676.031
Q119092.79
median36941.92
Q373623.22
95-th percentile135881.39
Maximum23834698
Range23827679
Interquartile range (IQR)54530.43

Descriptive statistics

Standard deviation1412434
Coefficient of variation (CV)7.9252499
Kurtosis151.77212
Mean178219.49
Median Absolute Deviation (MAD)21254.48
Skewness12.037697
Sum9.7183089 × 108
Variance1.9949699 × 1012
MonotonicityNot monotonic
2023-07-19T18:18:12.378030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95596.35 2
 
< 0.1%
9141.63 2
 
< 0.1%
22434.16 2
 
< 0.1%
34847.84 1
 
< 0.1%
59375.22 1
 
< 0.1%
33181.3 1
 
< 0.1%
79630.14 1
 
< 0.1%
16596.385 1
 
< 0.1%
8139.495 1
 
< 0.1%
17645.66 1
 
< 0.1%
Other values (5440) 5440
99.8%
ValueCountFrequency (%)
7019.435 1
< 0.1%
7020.545 1
< 0.1%
7021.91 1
< 0.1%
7023.16 1
< 0.1%
7056.405 1
< 0.1%
7059.455 1
< 0.1%
7079.32 1
< 0.1%
7087.24 1
< 0.1%
7087.38 1
< 0.1%
7097.015 1
< 0.1%
ValueCountFrequency (%)
23834698 1
< 0.1%
22225113 1
< 0.1%
21964843 1
< 0.1%
21752116 1
< 0.1%
21001046 1
< 0.1%
20501034 1
< 0.1%
20441835 1
< 0.1%
19768015 1
< 0.1%
19473209 1
< 0.1%
18884777 1
< 0.1%

monthly_inhand_salary
Real number (ℝ)

HIGH CORRELATION 

Distinct5451
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4188.661
Minimum319.55625
Maximum15167.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:12.593077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum319.55625
5-th percentile822.55645
Q11600.8342
median3059.78
Q36020.7267
95-th percentile10845.489
Maximum15167.18
Range14847.624
Interquartile range (IQR)4419.8925

Descriptive statistics

Standard deviation3227.2326
Coefficient of variation (CV)0.7704688
Kurtosis0.6238909
Mean4188.661
Median Absolute Deviation (MAD)1759.6404
Skewness1.1351174
Sum22840768
Variance10415030
MonotonicityNot monotonic
2023-07-19T18:18:12.831137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2295.058333 2
 
< 0.1%
6769.13 2
 
< 0.1%
3037.986667 1
 
< 0.1%
2391.618333 1
 
< 0.1%
2363.591667 1
 
< 0.1%
14929.54 1
 
< 0.1%
2739.108333 1
 
< 0.1%
6689.845 1
 
< 0.1%
1659.032083 1
 
< 0.1%
391.29125 1
 
< 0.1%
Other values (5441) 5441
99.8%
ValueCountFrequency (%)
319.55625 1
< 0.1%
357.2558333 1
< 0.1%
361.6033333 1
< 0.1%
368.3741667 1
< 0.1%
380.6491667 1
< 0.1%
382.7016667 1
< 0.1%
391.0533333 1
< 0.1%
391.29125 1
< 0.1%
391.89 1
< 0.1%
403.2541667 1
< 0.1%
ValueCountFrequency (%)
15167.18 1
< 0.1%
15136.69667 1
< 0.1%
15115.19 1
< 0.1%
15091.08667 1
< 0.1%
15090.07667 1
< 0.1%
14978.33667 1
< 0.1%
14929.54 1
< 0.1%
14880.38333 1
< 0.1%
14867.81333 1
< 0.1%
14866.44667 1
< 0.1%

num_bank_accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.396479
Minimum0
Maximum11
Zeros234
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:13.025692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5949614
Coefficient of variation (CV)0.48086195
Kurtosis-0.67964437
Mean5.396479
Median Absolute Deviation (MAD)2
Skewness-0.19419066
Sum29427
Variance6.7338247
MonotonicityNot monotonic
2023-07-19T18:18:13.200732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 727
13.3%
8 708
13.0%
7 695
12.7%
5 684
12.5%
4 684
12.5%
3 624
11.4%
9 305
5.6%
10 303
5.6%
1 254
 
4.7%
2 234
 
4.3%
Other values (2) 235
 
4.3%
ValueCountFrequency (%)
0 234
 
4.3%
1 254
 
4.7%
2 234
 
4.3%
3 624
11.4%
4 684
12.5%
5 684
12.5%
6 727
13.3%
7 695
12.7%
8 708
13.0%
9 305
5.6%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 303
5.6%
9 305
5.6%
8 708
13.0%
7 695
12.7%
6 727
13.3%
5 684
12.5%
4 684
12.5%
3 624
11.4%
2 234
 
4.3%

num_credit_card
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5640932
Minimum0
Maximum11
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:13.364773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0826137
Coefficient of variation (CV)0.37429526
Kurtosis-0.34082037
Mean5.5640932
Median Absolute Deviation (MAD)1
Skewness0.23011013
Sum30341
Variance4.3372798
MonotonicityNot monotonic
2023-07-19T18:18:13.541745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 1011
18.5%
6 924
16.9%
7 918
16.8%
4 806
14.8%
3 715
13.1%
10 289
 
5.3%
8 286
 
5.2%
9 265
 
4.9%
1 119
 
2.2%
2 116
 
2.1%
Other values (2) 4
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 119
 
2.2%
2 116
 
2.1%
3 715
13.1%
4 806
14.8%
5 1011
18.5%
6 924
16.9%
7 918
16.8%
8 286
 
5.2%
9 265
 
4.9%
ValueCountFrequency (%)
11 3
 
0.1%
10 289
 
5.3%
9 265
 
4.9%
8 286
 
5.2%
7 918
16.8%
6 924
16.9%
5 1011
18.5%
4 806
14.8%
3 715
13.1%
2 116
 
2.1%

interest_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.512379
Minimum1
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:13.754795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q320
95-th percentile31
Maximum498
Range497
Interquartile range (IQR)13

Descriptive statistics

Standard deviation20.513915
Coefficient of variation (CV)1.3224223
Kurtosis321.94352
Mean15.512379
Median Absolute Deviation (MAD)6
Skewness15.919623
Sum84589
Variance420.82069
MonotonicityNot monotonic
2023-07-19T18:18:13.981833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 284
 
5.2%
6 273
 
5.0%
8 256
 
4.7%
9 252
 
4.6%
12 245
 
4.5%
10 237
 
4.3%
7 229
 
4.2%
11 228
 
4.2%
17 226
 
4.1%
15 218
 
4.0%
Other values (40) 3005
55.1%
ValueCountFrequency (%)
1 155
2.8%
2 128
2.3%
3 174
3.2%
4 149
2.7%
5 284
5.2%
6 273
5.0%
7 229
4.2%
8 256
4.7%
9 252
4.6%
10 237
4.3%
ValueCountFrequency (%)
498 2
< 0.1%
482 1
< 0.1%
450 1
< 0.1%
443 1
< 0.1%
422 1
< 0.1%
377 1
< 0.1%
356 1
< 0.1%
332 1
< 0.1%
328 1
< 0.1%
288 1
< 0.1%

num_of_loan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5554741
Minimum0
Maximum9
Zeros631
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:14.163888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4645866
Coefficient of variation (CV)0.69318088
Kurtosis-0.65844018
Mean3.5554741
Median Absolute Deviation (MAD)2
Skewness0.42435382
Sum19388
Variance6.0741873
MonotonicityNot monotonic
2023-07-19T18:18:14.317920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 840
15.4%
2 839
15.4%
4 828
15.2%
0 631
11.6%
1 607
11.1%
6 463
8.5%
7 427
7.8%
5 418
7.7%
9 214
 
3.9%
8 186
 
3.4%
ValueCountFrequency (%)
0 631
11.6%
1 607
11.1%
2 839
15.4%
3 840
15.4%
4 828
15.2%
5 418
7.7%
6 463
8.5%
7 427
7.8%
8 186
 
3.4%
9 214
 
3.9%
ValueCountFrequency (%)
9 214
 
3.9%
8 186
 
3.4%
7 427
7.8%
6 463
8.5%
5 418
7.7%
4 828
15.2%
3 840
15.4%
2 839
15.4%
1 607
11.1%
0 631
11.6%

delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.214194
Minimum-5
Maximum67
Zeros71
Zeros (%)1.3%
Negative41
Negative (%)0.8%
Memory size85.2 KiB
2023-07-19T18:18:14.509952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.039273
Coefficient of variation (CV)0.708925
Kurtosis0.33448695
Mean21.214194
Median Absolute Deviation (MAD)9
Skewness0.96809751
Sum115681
Variance226.17972
MonotonicityNot monotonic
2023-07-19T18:18:14.733009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 206
 
3.8%
14 198
 
3.6%
7 190
 
3.5%
15 186
 
3.4%
10 174
 
3.2%
8 168
 
3.1%
6 165
 
3.0%
11 165
 
3.0%
16 164
 
3.0%
9 164
 
3.0%
Other values (62) 3673
67.4%
ValueCountFrequency (%)
-5 3
 
0.1%
-4 5
 
0.1%
-3 11
 
0.2%
-2 4
 
0.1%
-1 18
 
0.3%
0 71
1.3%
1 69
1.3%
2 68
1.2%
3 82
1.5%
4 109
2.0%
ValueCountFrequency (%)
67 1
 
< 0.1%
66 3
 
0.1%
65 7
 
0.1%
63 3
 
0.1%
62 28
0.5%
61 37
0.7%
60 36
0.7%
59 29
0.5%
58 26
0.5%
57 32
0.6%

num_of_delayed_payment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.48212
Minimum0
Maximum28
Zeros81
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:14.922359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q318
95-th percentile23
Maximum28
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.2077553
Coefficient of variation (CV)0.46044356
Kurtosis-0.65396067
Mean13.48212
Median Absolute Deviation (MAD)5
Skewness-0.19469995
Sum73518
Variance38.536226
MonotonicityNot monotonic
2023-07-19T18:18:15.106411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
17 329
 
6.0%
15 326
 
6.0%
16 324
 
5.9%
18 324
 
5.9%
19 319
 
5.8%
20 309
 
5.7%
8 296
 
5.4%
12 294
 
5.4%
10 290
 
5.3%
9 282
 
5.2%
Other values (19) 2360
43.3%
ValueCountFrequency (%)
0 81
 
1.5%
1 101
 
1.9%
2 125
2.3%
3 124
2.3%
4 108
 
2.0%
5 109
 
2.0%
6 139
2.5%
7 134
2.5%
8 296
5.4%
9 282
5.2%
ValueCountFrequency (%)
28 6
 
0.1%
27 14
 
0.3%
26 20
 
0.4%
25 115
 
2.1%
24 104
 
1.9%
23 114
 
2.1%
22 117
 
2.1%
21 162
3.0%
20 309
5.7%
19 319
5.8%

changed_credit_limit
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2326
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.260688
Minimum-6.13
Maximum34.85
Zeros103
Zeros (%)1.9%
Negative100
Negative (%)1.8%
Memory size85.2 KiB
2023-07-19T18:18:15.316445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.13
5-th percentile0.69
Q15.06
median9.22
Q314.87
95-th percentile24.1
Maximum34.85
Range40.98
Interquartile range (IQR)9.81

Descriptive statistics

Standard deviation7.0135965
Coefficient of variation (CV)0.68354059
Kurtosis0.024952795
Mean10.260688
Median Absolute Deviation (MAD)4.68
Skewness0.63064634
Sum55951.53
Variance49.190536
MonotonicityNot monotonic
2023-07-19T18:18:15.528501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
 
1.9%
10.06 10
 
0.2%
8.37 9
 
0.2%
5.38 9
 
0.2%
10.3 9
 
0.2%
11.73 9
 
0.2%
9.92 9
 
0.2%
11.6 9
 
0.2%
7.2 9
 
0.2%
10.47 9
 
0.2%
Other values (2316) 5268
96.6%
ValueCountFrequency (%)
-6.13 1
< 0.1%
-5.93 1
< 0.1%
-5.7 1
< 0.1%
-5.43 1
< 0.1%
-5.37 1
< 0.1%
-5.23 1
< 0.1%
-5.22 1
< 0.1%
-5.12 1
< 0.1%
-5.01 1
< 0.1%
-4.75 1
< 0.1%
ValueCountFrequency (%)
34.85 1
< 0.1%
33.74 1
< 0.1%
33.48 1
< 0.1%
32.22 1
< 0.1%
31.66 1
< 0.1%
31.6 1
< 0.1%
31.59 1
< 0.1%
31.58 1
< 0.1%
31.43 1
< 0.1%
31.15 1
< 0.1%

num_credit_inquiries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.657803
Minimum0
Maximum17
Zeros231
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:15.707539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q310
95-th percentile14
Maximum17
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9571592
Coefficient of variation (CV)0.59436411
Kurtosis-0.64609
Mean6.657803
Median Absolute Deviation (MAD)3
Skewness0.31279308
Sum36305
Variance15.659109
MonotonicityNot monotonic
2023-07-19T18:18:15.892579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 544
10.0%
6 499
 
9.2%
7 458
 
8.4%
5 457
 
8.4%
8 446
 
8.2%
3 428
 
7.8%
9 361
 
6.6%
2 360
 
6.6%
11 333
 
6.1%
10 317
 
5.8%
Other values (8) 1250
22.9%
ValueCountFrequency (%)
0 231
4.2%
1 294
5.4%
2 360
6.6%
3 428
7.8%
4 544
10.0%
5 457
8.4%
6 499
9.2%
7 458
8.4%
8 446
8.2%
9 361
6.6%
ValueCountFrequency (%)
17 30
 
0.6%
16 40
 
0.7%
15 95
 
1.7%
14 118
 
2.2%
13 165
 
3.0%
12 277
5.1%
11 333
6.1%
10 317
5.8%
9 361
6.6%
8 446
8.2%

outstanding_debt
Real number (ℝ)

HIGH CORRELATION 

Distinct5397
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1440.7918
Minimum0.34
Maximum4997.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:16.102523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile119.452
Q1573.52
median1169.57
Q31975.27
95-th percentile4097.456
Maximum4997.1
Range4996.76
Interquartile range (IQR)1401.75

Descriptive statistics

Standard deviation1166.4997
Coefficient of variation (CV)0.8096241
Kurtosis0.79627222
Mean1440.7918
Median Absolute Deviation (MAD)648.46
Skewness1.1829536
Sum7856637.5
Variance1360721.7
MonotonicityNot monotonic
2023-07-19T18:18:16.325373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
557.02 2
 
< 0.1%
3628.5 2
 
< 0.1%
1148.91 2
 
< 0.1%
291.71 2
 
< 0.1%
100.3 2
 
< 0.1%
796.88 2
 
< 0.1%
656.57 2
 
< 0.1%
699.36 2
 
< 0.1%
1456.12 2
 
< 0.1%
146.68 2
 
< 0.1%
Other values (5387) 5433
99.6%
ValueCountFrequency (%)
0.34 1
< 0.1%
0.54 1
< 0.1%
0.56 1
< 0.1%
0.77 1
< 0.1%
1.3 1
< 0.1%
1.42 1
< 0.1%
2.04 1
< 0.1%
3.31 1
< 0.1%
3.74 1
< 0.1%
4.82 1
< 0.1%
ValueCountFrequency (%)
4997.1 1
< 0.1%
4997.05 1
< 0.1%
4986.03 1
< 0.1%
4984.82 1
< 0.1%
4977.18 1
< 0.1%
4974.81 1
< 0.1%
4973.64 1
< 0.1%
4973.13 1
< 0.1%
4972.4 1
< 0.1%
4972.01 1
< 0.1%

credit_utilization_ratio
Real number (ℝ)

UNIQUE 

Distinct5453
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.381255
Minimum20.24413
Maximum48.199824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:16.537443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.24413
5-th percentile24.212463
Q128.11284
median32.454897
Q336.672843
95-th percentile40.268494
Maximum48.199824
Range27.955694
Interquartile range (IQR)8.5600031

Descriptive statistics

Standard deviation5.1501971
Coefficient of variation (CV)0.15904872
Kurtosis-0.95411908
Mean32.381255
Median Absolute Deviation (MAD)4.2873783
Skewness-0.0015470348
Sum176574.98
Variance26.52453
MonotonicityNot monotonic
2023-07-19T18:18:16.792651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.93385629 1
 
< 0.1%
28.29924023 1
 
< 0.1%
29.04432356 1
 
< 0.1%
33.70433844 1
 
< 0.1%
36.73889888 1
 
< 0.1%
35.37382318 1
 
< 0.1%
37.41455886 1
 
< 0.1%
32.57450143 1
 
< 0.1%
35.0975482 1
 
< 0.1%
26.25041788 1
 
< 0.1%
Other values (5443) 5443
99.8%
ValueCountFrequency (%)
20.24413035 1
< 0.1%
21.48488992 1
< 0.1%
21.49586225 1
< 0.1%
21.50221669 1
< 0.1%
21.55996832 1
< 0.1%
21.65476613 1
< 0.1%
21.66666958 1
< 0.1%
21.79611192 1
< 0.1%
21.8048219 1
< 0.1%
21.82315121 1
< 0.1%
ValueCountFrequency (%)
48.19982398 1
< 0.1%
47.17844594 1
< 0.1%
46.36094466 1
< 0.1%
46.2306829 1
< 0.1%
46.10143312 1
< 0.1%
45.9246553 1
< 0.1%
45.2558067 1
< 0.1%
45.10923491 1
< 0.1%
45.04290531 1
< 0.1%
44.96449393 1
< 0.1%

credit_history_age
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.079406
Minimum0
Maximum33
Zeros20
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:16.995708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q112
median18
Q325
95-th percentile32
Maximum33
Range33
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3521694
Coefficient of variation (CV)0.46197145
Kurtosis-0.90687676
Mean18.079406
Median Absolute Deviation (MAD)7
Skewness-0.037364366
Sum98587
Variance69.758734
MonotonicityNot monotonic
2023-07-19T18:18:17.199654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
18 300
 
5.5%
16 288
 
5.3%
17 261
 
4.8%
19 256
 
4.7%
20 227
 
4.2%
13 213
 
3.9%
29 188
 
3.4%
30 176
 
3.2%
11 176
 
3.2%
8 172
 
3.2%
Other values (24) 3196
58.6%
ValueCountFrequency (%)
0 20
 
0.4%
1 55
 
1.0%
2 73
1.3%
3 54
 
1.0%
4 48
 
0.9%
5 93
1.7%
6 170
3.1%
7 157
2.9%
8 172
3.2%
9 167
3.1%
ValueCountFrequency (%)
33 113
2.1%
32 163
3.0%
31 149
2.7%
30 176
3.2%
29 188
3.4%
28 162
3.0%
27 151
2.8%
26 170
3.1%
25 147
2.7%
24 165
3.0%

payment_of_min_amount
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.9 KiB
True
3277 
False
2176 
ValueCountFrequency (%)
True 3277
60.1%
False 2176
39.9%
2023-07-19T18:18:17.372694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

total_emi_per_month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4866
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.87095
Minimum0
Maximum5600
Zeros588
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:17.546742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.050423
median68.072981
Q3151.28725
95-th percentile357.27615
Maximum5600
Range5600
Interquartile range (IQR)121.23683

Descriptive statistics

Standard deviation193.39958
Coefficient of variation (CV)1.6407739
Kurtosis250.11268
Mean117.87095
Median Absolute Deviation (MAD)48.691099
Skewness11.615848
Sum642750.31
Variance37403.397
MonotonicityNot monotonic
2023-07-19T18:18:17.808800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 588
 
10.8%
18.81621457 1
 
< 0.1%
1320.790134 1
 
< 0.1%
34.55106016 1
 
< 0.1%
35.64449133 1
 
< 0.1%
37.95382531 1
 
< 0.1%
49.18230395 1
 
< 0.1%
155.8799843 1
 
< 0.1%
53.65909569 1
 
< 0.1%
116.8332019 1
 
< 0.1%
Other values (4856) 4856
89.1%
ValueCountFrequency (%)
0 588
10.8%
4.713183572 1
 
< 0.1%
4.865689677 1
 
< 0.1%
4.916138542 1
 
< 0.1%
5.138484696 1
 
< 0.1%
5.218466359 1
 
< 0.1%
5.262291048 1
 
< 0.1%
5.463308978 1
 
< 0.1%
5.629824417 1
 
< 0.1%
5.711416879 1
 
< 0.1%
ValueCountFrequency (%)
5600 1
< 0.1%
5041 1
< 0.1%
3871 1
< 0.1%
3494 1
< 0.1%
2591 1
< 0.1%
1775 1
< 0.1%
1701.955013 1
< 0.1%
1634.213281 1
< 0.1%
1614.855968 1
< 0.1%
1511.691963 1
< 0.1%

amount_invested_monthly
Real number (ℝ)

HIGH CORRELATION 

Distinct5442
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.74381
Minimum0
Maximum1977.3261
Zeros12
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:18.015675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.903501
Q171.620326
median128.69163
Q3235.85957
95-th percentile614.7951
Maximum1977.3261
Range1977.3261
Interquartile range (IQR)164.23924

Descriptive statistics

Standard deviation201.45539
Coefficient of variation (CV)1.0291788
Kurtosis8.8295422
Mean195.74381
Median Absolute Deviation (MAD)68.238474
Skewness2.5658002
Sum1067391
Variance40584.275
MonotonicityNot monotonic
2023-07-19T18:18:18.254717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
0.2%
40.24777032 1
 
< 0.1%
109.2575305 1
 
< 0.1%
299.2987505 1
 
< 0.1%
22.71440807 1
 
< 0.1%
774.4937886 1
 
< 0.1%
83.45669326 1
 
< 0.1%
432.0003782 1
 
< 0.1%
191.7249272 1
 
< 0.1%
24.68305746 1
 
< 0.1%
Other values (5432) 5432
99.6%
ValueCountFrequency (%)
0 12
0.2%
10.35874106 1
 
< 0.1%
10.61290788 1
 
< 0.1%
10.79480579 1
 
< 0.1%
10.92018009 1
 
< 0.1%
10.92486948 1
 
< 0.1%
11.1226776 1
 
< 0.1%
11.12510578 1
 
< 0.1%
11.19000242 1
 
< 0.1%
11.21879731 1
 
< 0.1%
ValueCountFrequency (%)
1977.326102 1
< 0.1%
1648.115331 1
< 0.1%
1599.935636 1
< 0.1%
1563.512893 1
< 0.1%
1523.290981 1
< 0.1%
1511.712436 1
< 0.1%
1410.679391 1
< 0.1%
1393.686271 1
< 0.1%
1388.950403 1
< 0.1%
1386.03197 1
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
Low_spent_Small_value_payments
1487 
High_spent_Medium_value_payments
1027 
High_spent_Large_value_payments
805 
Low_spent_Medium_value_payments
801 
High_spent_Small_value_payments
674 

Length

Max length32
Median length31
Mean length30.794792
Min length30

Characters and Unicode

Total characters167924
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_spent_Small_value_payments
2nd rowLow_spent_Medium_value_payments
3rd rowHigh_spent_Medium_value_payments
4th rowHigh_spent_Large_value_payments
5th rowLow_spent_Small_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 1487
27.3%
High_spent_Medium_value_payments 1027
18.8%
High_spent_Large_value_payments 805
14.8%
Low_spent_Medium_value_payments 801
14.7%
High_spent_Small_value_payments 674
12.4%
Low_spent_Large_value_payments 659
12.1%

Length

2023-07-19T18:18:18.998364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:18:19.199657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 1487
27.3%
high_spent_medium_value_payments 1027
18.8%
high_spent_large_value_payments 805
14.8%
low_spent_medium_value_payments 801
14.7%
high_spent_small_value_payments 674
12.4%
low_spent_large_value_payments 659
12.1%

Most occurring characters

ValueCountFrequency (%)
_ 21812
13.0%
e 19651
11.7%
a 14531
 
8.7%
s 10906
 
6.5%
p 10906
 
6.5%
n 10906
 
6.5%
t 10906
 
6.5%
l 9775
 
5.8%
m 9442
 
5.6%
u 7281
 
4.3%
Other values (13) 41808
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135206
80.5%
Connector Punctuation 21812
 
13.0%
Uppercase Letter 10906
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19651
14.5%
a 14531
10.7%
s 10906
8.1%
p 10906
8.1%
n 10906
8.1%
t 10906
8.1%
l 9775
 
7.2%
m 9442
 
7.0%
u 7281
 
5.4%
y 5453
 
4.0%
Other values (8) 25449
18.8%
Uppercase Letter
ValueCountFrequency (%)
L 4411
40.4%
H 2506
23.0%
S 2161
19.8%
M 1828
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 21812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 146112
87.0%
Common 21812
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19651
13.4%
a 14531
 
9.9%
s 10906
 
7.5%
p 10906
 
7.5%
n 10906
 
7.5%
t 10906
 
7.5%
l 9775
 
6.7%
m 9442
 
6.5%
u 7281
 
5.0%
y 5453
 
3.7%
Other values (12) 36355
24.9%
Common
ValueCountFrequency (%)
_ 21812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 21812
13.0%
e 19651
11.7%
a 14531
 
8.7%
s 10906
 
6.5%
p 10906
 
6.5%
n 10906
 
6.5%
t 10906
 
6.5%
l 9775
 
5.8%
m 9442
 
5.6%
u 7281
 
4.3%
Other values (13) 41808
24.9%

monthly_balance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5282
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389.70184
Minimum0
Maximum1463.7923
Zeros172
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:19.423700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile115.04303
Q1264.32493
median331.78825
Q3462.73499
95-th percentile849.96465
Maximum1463.7923
Range1463.7923
Interquartile range (IQR)198.41006

Descriptive statistics

Standard deviation219.07373
Coefficient of variation (CV)0.5621573
Kurtosis2.3642132
Mean389.70184
Median Absolute Deviation (MAD)85.738337
Skewness1.3195807
Sum2125044.1
Variance47993.301
MonotonicityNot monotonic
2023-07-19T18:18:19.657751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
3.2%
356.0781086 1
 
< 0.1%
315.3036256 1
 
< 0.1%
345.4126512 1
 
< 0.1%
301.3890767 1
 
< 0.1%
250.6413244 1
 
< 0.1%
352.2750567 1
 
< 0.1%
388.367812 1
 
< 0.1%
245.75404 1
 
< 0.1%
1146.00264 1
 
< 0.1%
Other values (5272) 5272
96.7%
ValueCountFrequency (%)
0 172
3.2%
1.77998453 1
 
< 0.1%
3.998674636 1
 
< 0.1%
6.241509617 1
 
< 0.1%
10.8846174 1
 
< 0.1%
11.08555403 1
 
< 0.1%
13.76047691 1
 
< 0.1%
16.78759108 1
 
< 0.1%
27.04311899 1
 
< 0.1%
28.61939774 1
 
< 0.1%
ValueCountFrequency (%)
1463.792328 1
< 0.1%
1460.917186 1
< 0.1%
1434.128246 1
< 0.1%
1400.426351 1
< 0.1%
1390.737797 1
< 0.1%
1383.981512 1
< 0.1%
1349.405313 1
< 0.1%
1317.772935 1
< 0.1%
1303.750446 1
< 0.1%
1298.487327 1
< 0.1%

credit_score
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
0
3864 
1
1589 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5453
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

Length

2023-07-19T18:18:19.864798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:18:20.014310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

Most occurring characters

ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5453
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5453
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5453
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3864
70.9%
1 1589
29.1%

segmento_edad
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.1 KiB
adulto
3289 
adulto joven
1870 
menor de edad
 
294

Length

Max length13
Median length6
Mean length8.4349899
Min length6

Characters and Unicode

Total characters45996
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadulto joven
2nd rowadulto
3rd rowadulto
4th rowadulto joven
5th rowadulto

Common Values

ValueCountFrequency (%)
adulto 3289
60.3%
adulto joven 1870
34.3%
menor de edad 294
 
5.4%

Length

2023-07-19T18:18:20.194350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:18:20.364486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
adulto 5159
65.2%
joven 1870
 
23.6%
menor 294
 
3.7%
de 294
 
3.7%
edad 294
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o 7323
15.9%
d 6041
13.1%
a 5453
11.9%
u 5159
11.2%
l 5159
11.2%
t 5159
11.2%
e 2752
 
6.0%
2458
 
5.3%
n 2164
 
4.7%
j 1870
 
4.1%
Other values (3) 2458
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43538
94.7%
Space Separator 2458
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7323
16.8%
d 6041
13.9%
a 5453
12.5%
u 5159
11.8%
l 5159
11.8%
t 5159
11.8%
e 2752
 
6.3%
n 2164
 
5.0%
j 1870
 
4.3%
v 1870
 
4.3%
Other values (2) 588
 
1.4%
Space Separator
ValueCountFrequency (%)
2458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43538
94.7%
Common 2458
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7323
16.8%
d 6041
13.9%
a 5453
12.5%
u 5159
11.8%
l 5159
11.8%
t 5159
11.8%
e 2752
 
6.3%
n 2164
 
5.0%
j 1870
 
4.3%
v 1870
 
4.3%
Other values (2) 588
 
1.4%
Common
ValueCountFrequency (%)
2458
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7323
15.9%
d 6041
13.1%
a 5453
11.9%
u 5159
11.2%
l 5159
11.2%
t 5159
11.2%
e 2752
 
6.0%
2458
 
5.3%
n 2164
 
4.7%
j 1870
 
4.1%
Other values (3) 2458
 
5.3%

prop_deuda_sueldo_anual
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5453
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062026046
Minimum1.6522987 × 10-6
Maximum0.64321679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:20.556530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6522987 × 10-6
5-th percentile0.0016916393
Q10.0092380612
median0.028543811
Q30.072750089
95-th percentile0.2490195
Maximum0.64321679
Range0.64321514
Interquartile range (IQR)0.063512028

Descriptive statistics

Standard deviation0.089323415
Coefficient of variation (CV)1.4400953
Kurtosis9.2904037
Mean0.062026046
Median Absolute Deviation (MAD)0.022648059
Skewness2.770602
Sum338.22803
Variance0.0079786725
MonotonicityNot monotonic
2023-07-19T18:18:20.781592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01736205171 1
 
< 0.1%
0.004127839696 1
 
< 0.1%
0.05209461579 1
 
< 0.1%
0.05044906715 1
 
< 0.1%
0.001012577799 1
 
< 0.1%
0.1057032124 1
 
< 0.1%
0.01423405761 1
 
< 0.1%
0.0850335781 1
 
< 0.1%
0.377907966 1
 
< 0.1%
0.1031726782 1
 
< 0.1%
Other values (5443) 5443
99.8%
ValueCountFrequency (%)
1.652298652 × 10-61
< 0.1%
3.604706779 × 10-61
< 0.1%
3.967860794 × 10-61
< 0.1%
4.16789002 × 10-61
< 0.1%
6.504125474 × 10-61
< 0.1%
6.686264248 × 10-61
< 0.1%
7.206332708 × 10-61
< 0.1%
7.219662666 × 10-61
< 0.1%
1.063963804 × 10-51
< 0.1%
1.262096633 × 10-51
< 0.1%
ValueCountFrequency (%)
0.6432167922 1
< 0.1%
0.6351466505 1
< 0.1%
0.6259347356 1
< 0.1%
0.6115905015 1
< 0.1%
0.6074824861 1
< 0.1%
0.6051222513 1
< 0.1%
0.6051065339 1
< 0.1%
0.603153352 1
< 0.1%
0.5994384849 1
< 0.1%
0.5945151143 1
< 0.1%

prop_emi_sueldo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4866
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031985851
Minimum0
Maximum0.61194542
Zeros588
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size85.2 KiB
2023-07-19T18:18:21.023761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.012670485
median0.025184789
Q30.042355816
95-th percentile0.084339114
Maximum0.61194542
Range0.61194542
Interquartile range (IQR)0.029685331

Descriptive statistics

Standard deviation0.031721013
Coefficient of variation (CV)0.99172014
Kurtosis45.258996
Mean0.031985851
Median Absolute Deviation (MAD)0.014679345
Skewness4.1503938
Sum174.41885
Variance0.0010062227
MonotonicityNot monotonic
2023-07-19T18:18:21.255821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 588
 
10.8%
0.0061936462 1
 
< 0.1%
0.1481872127 1
 
< 0.1%
0.005164702644 1
 
< 0.1%
0.02148511273 1
 
< 0.1%
0.09699635581 1
 
< 0.1%
0.02942455139 1
 
< 0.1%
0.01645395356 1
 
< 0.1%
0.04849827319 1
 
< 0.1%
0.02467472139 1
 
< 0.1%
Other values (4856) 4856
89.1%
ValueCountFrequency (%)
0 588
10.8%
0.004194888275 1
 
< 0.1%
0.004250725455 1
 
< 0.1%
0.004642771216 1
 
< 0.1%
0.004693256261 1
 
< 0.1%
0.004732171077 1
 
< 0.1%
0.004752923878 1
 
< 0.1%
0.004756042256 1
 
< 0.1%
0.00481709127 1
 
< 0.1%
0.004868627082 1
 
< 0.1%
ValueCountFrequency (%)
0.6119454245 1
< 0.1%
0.5143205785 1
< 0.1%
0.4534369031 1
< 0.1%
0.4503357414 1
< 0.1%
0.3080368979 1
< 0.1%
0.1983121228 1
< 0.1%
0.1941283062 1
< 0.1%
0.1940687388 1
< 0.1%
0.1930654684 1
< 0.1%
0.1905746093 1
< 0.1%

Interactions

2023-07-19T18:18:06.387509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:12.936451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.623560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.492846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.529351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:24.373842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.748392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.560189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:33.304326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:36.283062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:39.147565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.884518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:45.350769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:48.228526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:51.084587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:54.009310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.910093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.975319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.929392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:06.523547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:13.064426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.782507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.630859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.665520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:24.508197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.882425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.693219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:33.446346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:36.409087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:39.275606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:42.027261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:45.493334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:48.361469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:51.221679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:54.147340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:57.049135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:00.118348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:03.067418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:06.680861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:13.207426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.926540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.794914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.811558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:24.646235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:28.028473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.833767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:33.603462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:36.562112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:39.423635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:42.182296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:45.653018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:48.510512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:51.377704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:54.297372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:57.215168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-07-19T18:17:26.822322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:29.650268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:32.401112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:35.308846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.217556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:40.970327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:43.885890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:47.303698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.153463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.052110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:55.920000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:58.969496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:01.967327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:05.419122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:08.521950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:14.885756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:17.705894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:20.726510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:23.570762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:26.976473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:29.800532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:32.552146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:35.467866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.366157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.123348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:44.045921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:47.456746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.303497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.217132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.074025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.121524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.120325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:05.579112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:08.682323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.022792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:17.853930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:20.885060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:23.716325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.109515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:29.941571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:32.687183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:35.625909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.517178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.263379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:44.203954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:47.597479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.453041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.362172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.226074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.281559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.271300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:05.728135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:08.851023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.179827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.020495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.057105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:23.884376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.275404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.101589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:32.848213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:35.800955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.677214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.423426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:44.386002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:47.763807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.612073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.530203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.386434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.454599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.445297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:05.910300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:09.019051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.331492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.178536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.220288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:24.044412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.435308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.255122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:33.003246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:35.969316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.835509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.569460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:45.028178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:47.920838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.776129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.692246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.545569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.629638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.604311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:06.063334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:09.177087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:15.478531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:18.333564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:21.370323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:24.216815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:27.596344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:30.411150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:33.155281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:36.124357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:38.994531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:41.725489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:45.191717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:48.072189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:50.932553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:53.849287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:56.698023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:17:59.797683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:02.765347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:18:06.225369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-19T18:18:21.452857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ageannual_incomemonthly_inhand_salarynum_bank_accountsnum_credit_cardinterest_ratenum_of_loandelay_from_due_datenum_of_delayed_paymentchanged_credit_limitnum_credit_inquiriesoutstanding_debtcredit_utilization_ratiocredit_history_agetotal_emi_per_monthamount_invested_monthlymonthly_balanceprop_deuda_sueldo_anualprop_emi_sueldooccupationpayment_of_min_amountpayment_behaviourcredit_scoresegmento_edad
age1.0000.0880.083-0.171-0.140-0.199-0.187-0.151-0.163-0.129-0.219-0.1900.0190.212-0.0670.0570.106-0.168-0.1540.0210.3420.0090.1200.891
annual_income0.0881.0000.975-0.279-0.223-0.294-0.258-0.248-0.259-0.151-0.285-0.2930.1470.2890.4700.6380.539-0.746-0.2240.0090.0000.0270.0200.013
monthly_inhand_salary0.0830.9751.000-0.275-0.218-0.291-0.256-0.246-0.259-0.149-0.281-0.2920.1510.2860.4720.6510.554-0.722-0.2420.0140.3350.2000.1730.049
num_bank_accounts-0.171-0.279-0.2751.0000.4350.5890.4560.5630.5820.2970.4950.511-0.070-0.4920.107-0.199-0.2950.4760.3810.0000.6140.0450.2890.108
num_credit_card-0.140-0.223-0.2180.4351.0000.4670.3900.4460.3990.2010.4330.459-0.053-0.4080.113-0.139-0.2630.4150.3330.0080.4440.0340.3010.080
interest_rate-0.199-0.294-0.2910.5890.4671.0000.5290.5710.5830.3250.6130.608-0.074-0.5780.145-0.204-0.3350.5510.4410.0310.0220.0000.0000.000
num_of_loan-0.187-0.258-0.2560.4560.3900.5291.0000.4500.4550.2970.5390.568-0.088-0.6050.551-0.179-0.4740.5050.8500.0000.5670.0440.2820.125
delay_from_due_date-0.151-0.248-0.2460.5630.4460.5710.4501.0000.5510.2510.4960.524-0.054-0.4820.125-0.177-0.2790.4700.3730.0000.5310.0370.3270.080
num_of_delayed_payment-0.163-0.259-0.2590.5820.3990.5830.4550.5511.0000.2720.4870.493-0.073-0.4820.128-0.177-0.2960.4580.3810.0000.5990.0400.2260.096
changed_credit_limit-0.129-0.151-0.1490.2970.2010.3250.2970.2510.2721.0000.3390.313-0.020-0.3670.088-0.112-0.1700.2800.2460.0000.5000.0170.0460.083
num_credit_inquiries-0.219-0.285-0.2810.4950.4330.6130.5390.4960.4870.3391.0000.587-0.073-0.6100.159-0.184-0.3370.5360.4540.0000.6320.0400.3150.140
outstanding_debt-0.190-0.293-0.2920.5110.4590.6080.5680.5240.4930.3130.5871.000-0.058-0.6040.162-0.195-0.3460.8260.4710.0000.5700.0600.4110.123
credit_utilization_ratio0.0190.1470.151-0.070-0.053-0.074-0.088-0.054-0.073-0.020-0.073-0.0581.0000.0720.0260.0370.175-0.118-0.0730.0000.1090.0780.0390.010
credit_history_age0.2120.2890.286-0.492-0.408-0.578-0.605-0.482-0.482-0.367-0.610-0.6040.0721.000-0.1990.1890.360-0.543-0.5100.0000.6190.0530.2900.135
total_emi_per_month-0.0670.4700.4720.1070.1130.1450.5510.1250.1280.0880.1590.1620.026-0.1991.0000.3050.000-0.1530.6690.0190.0540.0340.0680.000
amount_invested_monthly0.0570.6380.651-0.199-0.139-0.204-0.179-0.177-0.177-0.112-0.184-0.1950.0370.1890.3051.000-0.028-0.476-0.1690.0140.1950.1520.0940.014
monthly_balance0.1060.5390.554-0.295-0.263-0.335-0.474-0.279-0.296-0.170-0.337-0.3460.1750.3600.000-0.0281.000-0.515-0.4360.0200.3420.2670.1640.052
prop_deuda_sueldo_anual-0.168-0.746-0.7220.4760.4150.5510.5050.4700.4580.2800.5360.826-0.118-0.543-0.153-0.476-0.5151.0000.4240.0130.4270.1190.2620.072
prop_emi_sueldo-0.154-0.224-0.2420.3810.3330.4410.8500.3730.3810.2460.4540.471-0.073-0.5100.669-0.169-0.4360.4241.0000.0000.1760.0280.0810.024
occupation0.0210.0090.0140.0000.0080.0310.0000.0000.0000.0000.0000.0000.0000.0000.0190.0140.0200.0130.0001.0000.0000.0000.0000.000
payment_of_min_amount0.3420.0000.3350.6140.4440.0220.5670.5310.5990.5000.6320.5700.1090.6190.0540.1950.3420.4270.1760.0001.0000.1020.2430.218
payment_behaviour0.0090.0270.2000.0450.0340.0000.0440.0370.0400.0170.0400.0600.0780.0530.0340.1520.2670.1190.0280.0000.1021.0000.1350.020
credit_score0.1200.0200.1730.2890.3010.0000.2820.3270.2260.0460.3150.4110.0390.2900.0680.0940.1640.2620.0810.0000.2430.1351.0000.073
segmento_edad0.8910.0130.0490.1080.0800.0000.1250.0800.0960.0830.1400.1230.0100.1350.0000.0140.0520.0720.0240.0000.2180.0200.0731.000

Missing values

2023-07-19T18:18:09.451955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-19T18:18:09.970059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idageoccupationannual_incomemonthly_inhand_salarynum_bank_accountsnum_credit_cardinterest_ratenum_of_loandelay_from_due_datenum_of_delayed_paymentchanged_credit_limitnum_credit_inquiriesoutstanding_debtcredit_utilization_ratiocredit_history_agepayment_of_min_amounttotal_emi_per_monthamount_invested_monthlypayment_behaviourmonthly_balancecredit_scoresegmento_edadprop_deuda_sueldo_anualprop_emi_sueldo
1CUS_0x21b128.0Teacher34847.843037.9866672461.034.05.422.0605.0332.93385627.0No18.816215218.904344Low_spent_Small_value_payments356.0781090adulto joven0.0173620.006194
6CUS_0x284a34.0Lawyer131313.4010469.2077590182.002.09.344.0352.1629.18791331.0No911.220179870.522382Low_spent_Medium_value_payments396.1113460adulto0.0026820.087038
7CUS_0x540730.0Media_Manager34081.382611.11500087153.03014.017.139.01704.1833.82348815.0Yes70.47833329.326364High_spent_Medium_value_payments411.3068041adulto0.0500030.026992
8CUS_0x415724.0Doctor114838.419843.8675002573.01111.08.248.01377.7427.81335421.0No226.892792254.571767High_spent_Large_value_payments742.9221910adulto joven0.0119970.023049
12CUS_0x3e4536.0Entrepreneur54392.164766.68000064143.0108.05.547.0179.2232.87880527.0Yes124.392082495.117898Low_spent_Small_value_payments147.1580200adulto0.0032950.026096
14CUS_0xff437.0Entrepreneur25546.262415.85500087145.01513.07.835.0758.4439.33348818.0Yes101.328637189.815861Low_spent_Medium_value_payments230.4410010adulto0.0296890.041943
15CUS_0x33d231.0Scientist31993.782942.1483336672.0816.06.281.0818.2236.01279417.0Yes45.14129897.588203Low_spent_Large_value_payments421.4853320adulto0.0255740.015343
16CUS_0x607020.0Accountant92047.087591.59000067160.01611.09.134.01296.6427.72764729.0Yes0.000000268.084603Low_spent_Large_value_payments761.0743971adulto joven0.0140870.000000
17CUS_0xfdb46.0Teacher32284.622898.38500067176.01312.02.2210.01283.3727.9301247.0Yes103.037560359.771832Low_spent_Small_value_payments117.0291070adulto0.0397520.035550
18CUS_0x355326.0Musician97791.427449.46934766120.0188.017.921.0107.4133.89444327.0Yes629.815653110.318204High_spent_Medium_value_payments947.6102960adulto joven0.0010980.084545
customer_idageoccupationannual_incomemonthly_inhand_salarynum_bank_accountsnum_credit_cardinterest_ratenum_of_loandelay_from_due_datenum_of_delayed_paymentchanged_credit_limitnum_credit_inquiriesoutstanding_debtcredit_utilization_ratiocredit_history_agepayment_of_min_amounttotal_emi_per_monthamount_invested_monthlypayment_behaviourmonthly_balancecredit_scoresegmento_edadprop_deuda_sueldo_anualprop_emi_sueldo
12478CUS_0x2c0a45.0Doctor32625.592922.7991670584.0710.07.558.0177.9036.54340229.0No73.125008123.766491High_spent_Medium_value_payments0.00adulto0.0054530.025019
12483CUS_0x1d1844.0Musician33702.742998.56166773192.067.010.705.0636.9623.75205324.0Yes37.993229246.334242Low_spent_Small_value_payments0.00adulto0.0188990.012670
12485CUS_0x47fa31.0Mechanic64511.345440.94500073150.078.018.572.0330.6035.25141623.0Yes0.000000535.193284Low_spent_Small_value_payments0.00adulto0.0051250.000000
12486CUS_0x89aa39.0Manager85744.127125.34333367112.02918.018.085.0717.7940.55497815.0Yes72.099176537.048216Low_spent_Large_value_payments0.00adulto0.0083710.010119
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